FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking

The field of automated fact-checking increasingly depends on retrieving web-based evidence to determine the veracity of claims in real-world scenarios. A significant challenge in this process is not only retrieving relevant information, but also identifying evidence that can both support and refute complex claims. Traditional retrieval methods may return documents that directly address claims or lean toward supporting them, but often struggle with more complex claims requiring indirect reasoning. While some existing benchmarks and methods target retrieval for fact-checking, a comprehensive real-world open-domain benchmark has been lacking. In this paper, we present a real-world retrieval benchmark FactIR, derived from Factiverse production logs, enhanced with human annotations. We rigorously evaluate state-of-the-art retrieval models in a zero-shot setup on FactIR and offer insights for developing practical retrieval systems for fact-checking. Code and data are available at https://github.com/factiverse/factIR.

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Publication Details

Published
2025-05-08
DOI
https://doi.org/10.1145/3701716.3715300
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
2.48

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article

FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking

Vinay Setty, V Venktesh
1 citations
Topic Modeling
2.48
article

FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking

Vinay Setty, V Venktesh
article en
1 citations

Abstract

The field of automated fact-checking increasingly depends on retrieving web-based evidence to determine the veracity of claims in real-world scenarios. A significant challenge in this process is not only retrieving relevant information, but also identifying evidence that can both support and refute complex claims. Traditional retrieval methods may return documents that directly address claims or lean toward supporting them, but often struggle with more complex claims requiring indirect reasoning. While some existing benchmarks and methods target retrieval for fact-checking, a comprehensive real-world open-domain benchmark has been lacking. In this paper, we present a real-world retrieval benchmark FactIR, derived from Factiverse production logs, enhanced with human annotations. We rigorously evaluate state-of-the-art retrieval models in a zero-shot setup on FactIR and offer insights for developing practical retrieval systems for fact-checking. Code and data are available at https://github.com/factiverse/factIR.

University of Stavanger (NO), Delft University of Technology (NL)
Norges Forskningsråd
Openalex Percentile: Top 10%
Topic Modeling
2.48
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